{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 十四款常规机器学习建模\n",
    "\n",
    "参考案例一：[module-sklearn.ensemble](http://scikit-learn.org/stable/modules/classes.html#module-sklearn.ensemble)\n",
    "\n",
    "参考案例二：[ensemble-plot-feature-transformation](http://scikit-learn.org/stable/auto_examples/ensemble/plot_feature_transformation.html#sphx-glr-auto-examples-ensemble-plot-feature-transformation-py)\n",
    "\n",
    "\n",
    "练习步骤为：\n",
    "\n",
    "- 1、随机准备数据make_classification\n",
    "- 2、两套模型的训练与基本信息准备\n",
    "- 3、观察14套模型的准确率与召回率\n",
    "- 4、刻画14套模型的calibration plots校准曲线\n",
    "- 5、14套模型的重要性输出\n",
    "- 6、14套模型的ROC值计算与plot\n",
    "- 7、加权模型融合数据准备\n",
    "- 8、基准优化策略：14套模型融合——平均\n",
    "- 9、加权平均优化策略：14套模型融合——加权平均优化\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 函数加载\n",
    "\n",
    "\n",
    "import numpy as np\n",
    "import sklearn.metrics as metrics\n",
    "from scipy.optimize import minimize\n",
    "from sklearn.metrics import log_loss\n",
    "from sklearn import metrics\n",
    "\n",
    "def MinimiseOptimize(preds,models_filenames,nb_classes,sample_N,testY,NUM_TESTS = 20):\n",
    "    '''\n",
    "    preds ,此时为cifar_100 (6,10000, 100)\n",
    "    testY,类别列表，100分类，就是：[[1],[2],[3],...],(100,1)\n",
    "    nb_classesf,分类数,100\n",
    "    models_filenames模型名称,5\n",
    "    NUM_TESTS , 迭代次数，默认为20次\n",
    "    '''\n",
    "    best_acc = 0.0\n",
    "    best_weights = None\n",
    "    # Parameters for optimization\n",
    "    constraints = ({'type': 'eq', 'fun':lambda w: 1 - sum(w)})\n",
    "    bounds = [(0, 1)] * len(preds)\n",
    "\n",
    "    # Check for NUM_TESTS times\n",
    "    for iteration in range(NUM_TESTS):  # NUM_TESTS,迭代次数为25\n",
    "        # Random initialization of weights\n",
    "        prediction_weights = np.random.random(len(models_filenames))\n",
    "\n",
    "        # Minimise the loss \n",
    "        result = minimize(log_loss_func, prediction_weights, method='SLSQP', bounds=bounds, constraints=constraints)\n",
    "\n",
    "        weights = result['x']\n",
    "        weighted_predictions = np.zeros((sample_N, nb_classes), dtype='float32')\n",
    "\n",
    "        # Calculate weighted predictions\n",
    "        for weight, prediction in zip(weights, preds):\n",
    "            weighted_predictions += weight * prediction\n",
    "\n",
    "        yPred = np.argmax(weighted_predictions, axis=1)\n",
    "        yTrue = testY\n",
    "\n",
    "        # Calculate weight prediction accuracy\n",
    "        accuracy = metrics.accuracy_score(yTrue, yPred) * 100\n",
    "        recall = recall_score(yTrue, yPred)\n",
    "\n",
    "        print('\\n ------- Iteration : %d  - acc: %s  - rec:%s -------  '%((iteration + 1),accuracy,recall))\n",
    "        print('    Best Ensemble Weights: \\n',result['x'])\n",
    "        \n",
    "        # Save current best weights \n",
    "        if accuracy > best_acc:\n",
    "            best_acc = accuracy\n",
    "            best_weights = weights\n",
    "    return best_acc,best_weights\n",
    "\n",
    "# Create the loss metric \n",
    "def log_loss_func(weights):\n",
    "    ''' scipy minimize will pass the weights as a numpy array '''\n",
    "    final_prediction = np.zeros((sample_N, nb_classes), dtype='float32')\n",
    "\n",
    "    for weight, prediction in zip(weights, preds):\n",
    "        final_prediction += weight * prediction\n",
    "\n",
    "    return log_loss(testY_cat, final_prediction)\n",
    "\n",
    "# calculate accuracy/recall\n",
    "\n",
    "def calculate_weighted_accuracy(prediction_weights):\n",
    "    '''计算acc/recall 以及得到预测结果'''\n",
    "    weighted_predictions = np.zeros((sample_N, nb_classes), dtype='float32')\n",
    "    for weight, prediction in zip(prediction_weights, preds):\n",
    "        weighted_predictions += weight * prediction\n",
    "    yPred = np.argmax(weighted_predictions, axis=1)\n",
    "    yTrue = testY\n",
    "    accuracy = metrics.accuracy_score(yTrue, yPred) * 100\n",
    "    recall = recall_score(yTrue, yPred)\n",
    "    print(\"Accuracy : \", accuracy)\n",
    "    print(\"Recall : \", recall)\n",
    "    \n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1、随机准备数据make_classification"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Automatically created module for IPython interactive environment\n",
      "(6400, 20) (2000, 20) (6400,) (2000,)\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "np.random.seed(10)\n",
    "%matplotlib inline \n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "from sklearn.datasets import make_classification\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.ensemble import (RandomTreesEmbedding, RandomForestClassifier,\n",
    "                              GradientBoostingClassifier)\n",
    "from sklearn.preprocessing import OneHotEncoder\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import roc_curve,accuracy_score,recall_score\n",
    "from sklearn.pipeline import make_pipeline\n",
    "from sklearn.calibration import calibration_curve\n",
    "import copy\n",
    "print(__doc__)\n",
    "from matplotlib.colors import ListedColormap\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.datasets import make_moons, make_circles, make_classification\n",
    "from sklearn.neural_network import MLPClassifier\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.gaussian_process import GaussianProcessClassifier\n",
    "from sklearn.gaussian_process.kernels import RBF\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier, AdaBoostClassifier\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "from sklearn.discriminant_analysis import QuadraticDiscriminantAnalysis\n",
    "\n",
    "def yLabel(y_pred):\n",
    "    y_pred_f = copy.copy(y_pred)\n",
    "    y_pred_f[y_pred_f>=0.5] = 1\n",
    "    y_pred_f[y_pred_f<0.5] = 0\n",
    "    return y_pred_f\n",
    "\n",
    "def acc_recall(y_test, y_pred_rf):\n",
    "    return {'accuracy': accuracy_score(y_test, yLabel(y_pred_rf)), \\\n",
    "            'recall': recall_score(y_test, yLabel(y_pred_rf))}\n",
    "\n",
    "# 数据制作\n",
    "X, y = make_classification(n_samples=10000)\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2,random_state = 4000)  # 对半分\n",
    "X_train, X_train_lr, y_train, y_train_lr = train_test_split(X_train,\n",
    "                                                            y_train,\n",
    "                                                            test_size=0.2,random_state = 4000)\n",
    "\n",
    "print(X_train.shape, X_test.shape, y_train.shape, y_test.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2.1 八款主流机器学习模型\n",
    "\n",
    "参考 [classifier-comparison](http://scikit-learn.org/stable/auto_examples/classification/plot_classifier_comparison.html#sphx-glr-auto-examples-classification-plot-classifier-comparison-py)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " --- Start Model : Nearest Neighbors ----\n",
      "\n",
      "Wall time: 50.9 ms\n",
      "Wall time: 862 ms\n",
      "\n",
      " --- Start Model : Linear SVM ----\n",
      "\n",
      "Wall time: 630 ms\n",
      "Wall time: 78.9 ms\n",
      "\n",
      " --- Start Model : RBF SVM ----\n",
      "\n",
      "Wall time: 4.46 s\n",
      "Wall time: 568 ms\n",
      "\n",
      " --- Start Model : Decision Tree ----\n",
      "\n",
      "Wall time: 80.3 ms\n",
      "Wall time: 0 ns\n",
      "\n",
      " --- Start Model : Neural Net ----\n",
      "\n",
      "Wall time: 4.72 s\n",
      "Wall time: 7.98 ms\n",
      "\n",
      " --- Start Model : AdaBoost ----\n",
      "\n",
      "Wall time: 1.25 s\n",
      "Wall time: 30.9 ms\n",
      "\n",
      " --- Start Model : Naive Bayes ----\n",
      "\n",
      "Wall time: 4.91 ms\n",
      "Wall time: 2.55 ms\n",
      "\n",
      " --- Start Model : QDA ----\n",
      "\n",
      "Wall time: 8.05 ms\n",
      "Wall time: 997 µs\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\YZHENG\\Anaconda3\\lib\\site-packages\\sklearn\\discriminant_analysis.py:715: UserWarning: Variables are collinear\n",
      "  warnings.warn(\"Variables are collinear\")\n"
     ]
    }
   ],
   "source": [
    "\n",
    "h = .02  # step size in the mesh\n",
    "names = [\"Nearest Neighbors\", \"Linear SVM\", \"RBF SVM\",\n",
    "         \"Decision Tree\", \"Neural Net\", \"AdaBoost\",\n",
    "         \"Naive Bayes\", \"QDA\"]\n",
    "# 去掉\"Gaussian Process\"，太耗时，是其他的300倍以上\n",
    "\n",
    "classifiers = [\n",
    "    KNeighborsClassifier(3),\n",
    "    SVC(kernel=\"linear\", C=0.025),\n",
    "    SVC(gamma=2, C=1),\n",
    "    #GaussianProcessClassifier(1.0 * RBF(1.0)),\n",
    "    DecisionTreeClassifier(max_depth=5),\n",
    "    #RandomForestClassifier(max_depth=5, n_estimators=10, max_features=1),\n",
    "    MLPClassifier(alpha=1),\n",
    "    AdaBoostClassifier(),\n",
    "    GaussianNB(),\n",
    "    QuadraticDiscriminantAnalysis()]\n",
    "\n",
    "predictEight = {}\n",
    "for name, clf in zip(names, classifiers):\n",
    "    predictEight[name] = {}\n",
    "    predictEight[name]['prob_pos'],predictEight[name]['fpr_tpr'],predictEight[name]['acc_recall'] = [],[],[]\n",
    "    predictEight[name]['importance'] = []\n",
    "    print('\\n --- Start Model : %s ----\\n'%name)\n",
    "    %time clf.fit(X_train, y_train)\n",
    "    # Plot the decision boundary. For that, we will assign a color to each\n",
    "    # point in the mesh [x_min, x_max]x[y_min, y_max].\n",
    "    if hasattr(clf, \"decision_function\"):\n",
    "        %time prob_pos = clf.decision_function(X_test)\n",
    "        # # The confidence score for a sample is the signed distance of that sample to the hyperplane.\n",
    "    else:\n",
    "        %time prob_pos= clf.predict_proba(X_test)[:, 1]\n",
    "        prob_pos = (prob_pos - prob_pos.min()) / (prob_pos.max() - prob_pos.min())\n",
    "        # 需要归一化\n",
    "    predictEight[name]['prob_pos'] = prob_pos\n",
    "    \n",
    "    # 计算ROC、acc、recall\n",
    "    predictEight[name]['fpr_tpr'] = roc_curve(y_test, prob_pos)[:2]\n",
    "    predictEight[name]['acc_recall'] = acc_recall(y_test, prob_pos)  # 计算准确率与召回\n",
    "    \n",
    "    # 提取重要性信息\n",
    "    if hasattr(clf, \"coef_\"):\n",
    "        predictEight[name]['importance'] = clf.coef_\n",
    "    elif hasattr(clf, \"feature_importances_\"):\n",
    "        predictEight[name]['importance'] = clf.feature_importances_\n",
    "    elif hasattr(clf, \"sigma_\"):\n",
    "        predictEight[name]['importance'] = clf.sigma_\n",
    "        # variance of each feature per class"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2.2 组合模型延伸\n",
    "\n",
    "参考官网[feature-transformation](http://scikit-learn.org/stable/auto_examples/ensemble/plot_feature_transformation.html#sphx-glr-auto-examples-ensemble-plot-feature-transformation-py)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LM 开始计算...\n",
      "Wall time: 12 ms\n",
      "随机森林编码+LM 开始计算...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\YZHENG\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:762: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wall time: 771 ms\n",
      "\n",
      " 随机森林系列 开始计算... \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\YZHENG\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:762: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wall time: 181 ms\n",
      "\n",
      " 梯度提升树系列 开始计算... \n",
      "Wall time: 198 ms\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\YZHENG\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:762: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n"
     ]
    }
   ],
   "source": [
    "n_estimator = 100\n",
    "\n",
    "'''\n",
    "model 0 : lm\n",
    "logistic\n",
    "'''\n",
    "print('LM 开始计算...')\n",
    "lm = LogisticRegression()\n",
    "%time lm.fit(X_train, y_train)\n",
    "y_pred_lm = lm.predict_proba(X_test)[:, 1]\n",
    "fpr_lm, tpr_lm, _ = roc_curve(y_test, y_pred_lm)\n",
    "lm_ar = acc_recall(y_test, y_pred_lm)  # 计算准确率与召回\n",
    "\n",
    "'''\n",
    "model 1 : rt + lm\n",
    "无监督变换 + lg\n",
    "'''\n",
    "# Unsupervised transformation based on totally random trees\n",
    "print('随机森林编码+LM 开始计算...')\n",
    "rt = RandomTreesEmbedding(max_depth=3, n_estimators=n_estimator,\n",
    "    random_state=0)\n",
    "# 数据集的无监督变换到高维稀疏表示。\n",
    "\n",
    "rt_lm = LogisticRegression()\n",
    "pipeline = make_pipeline(rt, rt_lm)\n",
    "%time pipeline.fit(X_train, y_train)\n",
    "y_pred_rt = pipeline.predict_proba(X_test)[:, 1]\n",
    "fpr_rt_lm, tpr_rt_lm, _ = roc_curve(y_test, y_pred_rt)\n",
    "rt_lm_ar = acc_recall(y_test, y_pred_rt)  # 计算准确率与召回\n",
    "\n",
    "'''\n",
    "model 2 : RF / RF+LM\n",
    "'''\n",
    "print('\\n 随机森林系列 开始计算... ')\n",
    "# Supervised transformation based on random forests\n",
    "rf = RandomForestClassifier(max_depth=3, n_estimators=n_estimator)\n",
    "rf_enc = OneHotEncoder()\n",
    "rf_lm = LogisticRegression()\n",
    "rf.fit(X_train, y_train)\n",
    "rf_enc.fit(rf.apply(X_train))  # rf.apply(X_train)-(1310, 100)     X_train-(1310, 20)\n",
    "# 用100棵树的信息作为X，载入做LM模型\n",
    "%time rf_lm.fit(rf_enc.transform(rf.apply(X_train_lr)), y_train_lr)\n",
    "y_pred_rf_lm = rf_lm.predict_proba(rf_enc.transform(rf.apply(X_test)))[:, 1]\n",
    "fpr_rf_lm, tpr_rf_lm, _ = roc_curve(y_test, y_pred_rf_lm)\n",
    "rf_lm_ar = acc_recall(y_test, y_pred_rf_lm)  # 计算准确率与召回\n",
    "\n",
    "'''\n",
    "model 2 : GRD / GRD + LM\n",
    "'''\n",
    "print('\\n 梯度提升树系列 开始计算... ')\n",
    "grd = GradientBoostingClassifier(n_estimators=n_estimator)\n",
    "grd_enc = OneHotEncoder()\n",
    "grd_lm = LogisticRegression()\n",
    "grd.fit(X_train, y_train)\n",
    "grd_enc.fit(grd.apply(X_train)[:, :, 0])\n",
    "%time grd_lm.fit(grd_enc.transform(grd.apply(X_train_lr)[:, :, 0]), y_train_lr)\n",
    "\n",
    "y_pred_grd_lm = grd_lm.predict_proba(\n",
    "    grd_enc.transform(grd.apply(X_test)[:, :, 0]))[:, 1]\n",
    "fpr_grd_lm, tpr_grd_lm, _ = roc_curve(y_test, y_pred_grd_lm)\n",
    "grd_lm_ar = acc_recall(y_test, y_pred_grd_lm)  # 计算准确率与召回\n",
    "\n",
    "# The gradient boosted model by itself\n",
    "y_pred_grd = grd.predict_proba(X_test)[:, 1]\n",
    "fpr_grd, tpr_grd, _ = roc_curve(y_test, y_pred_grd)\n",
    "grd_ar = acc_recall(y_test, y_pred_grd)  # 计算准确率与召回\n",
    "\n",
    "# The random forest model by itself\n",
    "y_pred_rf = rf.predict_proba(X_test)[:, 1]\n",
    "fpr_rf, tpr_rf, _ = roc_curve(y_test, y_pred_rf)\n",
    "rf_ar = acc_recall(y_test, y_pred_rf)  # 计算准确率与召回"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3、观察14套模型的准确率与召回率"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "----- 第一套,8款常规机器学习模型 -----\n",
      "\n",
      " ----- The Model  : Nearest Neighbors , -----\n",
      " \n",
      "{'accuracy': 0.8315, 'recall': 0.8446014127144299}\n",
      "\n",
      " ----- The Model  : Linear SVM , -----\n",
      " \n",
      "{'accuracy': 0.891, 'recall': 0.8456104944500504}\n",
      "\n",
      " ----- The Model  : RBF SVM , -----\n",
      " \n",
      "{'accuracy': 0.5045, 'recall': 0.0}\n",
      "\n",
      " ----- The Model  : Decision Tree , -----\n",
      " \n",
      "{'accuracy': 0.8995, 'recall': 0.9091826437941474}\n",
      "\n",
      " ----- The Model  : Neural Net , -----\n",
      " \n",
      "{'accuracy': 0.9015, 'recall': 0.917255297679112}\n",
      "\n",
      " ----- The Model  : AdaBoost , -----\n",
      " \n",
      "{'accuracy': 0.518, 'recall': 0.028254288597376387}\n",
      "\n",
      " ----- The Model  : Naive Bayes , -----\n",
      " \n",
      "{'accuracy': 0.893, 'recall': 0.9152371342078708}\n",
      "\n",
      " ----- The Model  : QDA , -----\n",
      " \n",
      "{'accuracy': 0.802, 'recall': 0.8284561049445005}\n",
      "\n",
      " ----- 第二套,6款组合机器学习模型 -----\n",
      " \n",
      "\n",
      " --- LM 准确率与召回为: ---- \n",
      "  {'accuracy': 0.9005, 'recall': 0.9152371342078708}\n",
      "\n",
      " --- LM + RT 准确率与召回为: ---- \n",
      "  {'accuracy': 0.892, 'recall': 0.875882946518668}\n",
      "\n",
      " --- LM + RF 准确率与召回为: ---- \n",
      "  {'accuracy': 0.892, 'recall': 0.9031281533804238}\n",
      "\n",
      " --- GBT + LM 准确率与召回为: ---- \n",
      "  {'accuracy': 0.89, 'recall': 0.8970736629667003}\n",
      "\n",
      " --- GBT 准确率与召回为: ---- \n",
      "  {'accuracy': 0.898, 'recall': 0.9041372351160444}\n",
      "\n",
      " --- RF 准确率与召回为: ---- \n",
      "  {'accuracy': 0.903, 'recall': 0.8990918264379415}\n"
     ]
    }
   ],
   "source": [
    "print('----- 第一套,8款常规机器学习模型 -----')\n",
    "for x,y in predictEight.items():\n",
    "    print('\\n ----- The Model  : %s , -----\\n '%(x)  )\n",
    "    print(predictEight[x]['acc_recall'])\n",
    "\n",
    "print('\\n ----- 第二套,6款组合机器学习模型 -----\\n ')\n",
    "names = ['LM','LM + RT','LM + RF','GBT + LM','GBT','RF']\n",
    "ar_list = [lm_ar,rt_lm_ar,rf_lm_ar,grd_lm_ar,grd_ar,rf_ar]\n",
    "for x,y in zip(names,ar_list):\n",
    "    print('\\n --- %s 准确率与召回为: ---- \\n '%x,y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4、刻画14套模型的calibration plots校准曲线\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x1080 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# #############################################################################\n",
    "# Plot calibration plots\n",
    "names = [\"Nearest Neighbors\", \"Linear SVM\", \"RBF SVM\",\n",
    "         \"Decision Tree\", \"Neural Net\", \"AdaBoost\",\n",
    "         \"Naive Bayes\", \"QDA\"]\n",
    "\n",
    "plt.figure(figsize=(15, 15))\n",
    "ax1 = plt.subplot2grid((3, 1), (0, 0), rowspan=2)\n",
    "ax2 = plt.subplot2grid((3, 1), (2, 0))\n",
    "\n",
    "ax1.plot([0, 1], [0, 1], \"k:\", label=\"Perfectly calibrated\")\n",
    "for prob_pos, name in [[predictEight[n]['prob_pos'],n] for n in names] + [(y_pred_lm,'LM'),\n",
    "                       (y_pred_rt,'RT + LM'),\n",
    "                       (y_pred_rf_lm,'RF + LM'),\n",
    "                       (y_pred_grd_lm,'GBT + LM'),\n",
    "                       (y_pred_grd,'GBT'),\n",
    "                       (y_pred_rf,'RF')]:\n",
    "    \n",
    "    prob_pos = (prob_pos - prob_pos.min()) / (prob_pos.max() - prob_pos.min())\n",
    "        \n",
    "    fraction_of_positives, mean_predicted_value = calibration_curve(y_test, prob_pos, n_bins=10)\n",
    "\n",
    "    ax1.plot(mean_predicted_value, fraction_of_positives, \"s-\",\n",
    "             label=\"%s\" % (name, ))\n",
    "\n",
    "    ax2.hist(prob_pos, range=(0, 1), bins=10, label=name,\n",
    "             histtype=\"step\", lw=2)\n",
    "\n",
    "ax1.set_ylabel(\"Fraction of positives\")\n",
    "ax1.set_ylim([-0.05, 1.05])\n",
    "ax1.legend(loc=\"lower right\")\n",
    "ax1.set_title('Calibration plots  (reliability curve)')\n",
    "\n",
    "ax2.set_xlabel(\"Mean predicted value\")\n",
    "ax2.set_ylabel(\"Count\")\n",
    "ax2.legend(loc=\"upper center\", ncol=2)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5、14套模型的重要性输出\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " -------- RadomFree importances ------------\n",
      "\n",
      "[0.00296091 0.00342745 0.01563262 0.00604974 0.0034983  0.07484716\n",
      " 0.00225245 0.00320943 0.00864947 0.00636088 0.00102355 0.07940459\n",
      " 0.0054721  0.0037093  0.00189582 0.22083259 0.00918668 0.00374317\n",
      " 0.54725516 0.00058863]\n",
      "\n",
      " -------- GradientBoosting importances ------------\n",
      "\n",
      "[0.00216315 0.00164217 0.00414873 0.00262191 0.00313331 0.00747766\n",
      " 0.00190969 0.00233502 0.00285445 0.00150632 0.00115527 0.0093479\n",
      " 0.00351502 0.00191607 0.00113117 0.01832198 0.00253518 0.00121355\n",
      " 0.92930677 0.00176468]\n",
      "\n",
      " -------- Logistic Coefficient  ------------\n",
      "\n",
      "[[-0.0428897  -0.03642022 -0.04947043  0.13252639  0.02648274 -0.17968183\n",
      "  -0.00578436 -0.00514013  0.01951968 -0.02658481 -0.06581354 -0.49472675\n",
      "   0.02783772 -0.09476863 -0.03198238  0.97092936  0.10570689  0.06479152\n",
      "   2.33430639 -0.05842235]]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-7-a661cabe8053>:12: DeprecationWarning: elementwise comparison failed; this will raise an error in the future.\n",
      "  [[predictEight[n]['importance'],n] for n in eight_names if predictEight[n]['importance'] != [] ]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[[array([[-0.01216941, -0.00803642, -0.0371962 ,  0.0440979 ,  0.02813782,\n",
       "          -0.11937585, -0.01560251,  0.00472539,  0.00288186, -0.02019184,\n",
       "          -0.03446023, -0.3192126 ,  0.01783739, -0.03038167, -0.03201148,\n",
       "           0.5766335 ,  0.0523026 ,  0.0390042 ,  1.41965647, -0.02905152]]),\n",
       "  'Linear SVM'],\n",
       " [array([2.09875561e-03, 0.00000000e+00, 1.25998010e-03, 0.00000000e+00,\n",
       "         7.28734587e-04, 1.89254296e-02, 1.31368357e-03, 1.01971475e-03,\n",
       "         3.11957777e-03, 0.00000000e+00, 0.00000000e+00, 1.24014043e-03,\n",
       "         2.29969029e-03, 0.00000000e+00, 1.06086399e-03, 5.01131094e-03,\n",
       "         2.01052164e-03, 9.20372735e-04, 9.58991224e-01, 0.00000000e+00]),\n",
       "  'Decision Tree'],\n",
       " [array([0.02, 0.06, 0.08, 0.02, 0.  , 0.04, 0.04, 0.  , 0.02, 0.04, 0.  ,\n",
       "         0.1 , 0.  , 0.04, 0.  , 0.18, 0.04, 0.02, 0.3 , 0.  ]),\n",
       "  'AdaBoost'],\n",
       " [array([[0.99215286, 1.00848734, 1.06211506, 1.0240741 , 0.98769432,\n",
       "          0.4869747 , 1.02797477, 1.01967028, 0.95005496, 0.93633662,\n",
       "          0.99736016, 1.60130181, 1.00589126, 0.97670992, 1.01558554,\n",
       "          1.20659868, 1.00973942, 0.99938876, 0.89444533, 0.97622153],\n",
       "         [1.03603107, 1.03187653, 0.96780999, 0.96882558, 0.96528512,\n",
       "          0.43647263, 1.01035563, 1.01598846, 0.9969073 , 1.00241327,\n",
       "          0.99849963, 1.43008874, 0.97184961, 1.02087461, 0.9725773 ,\n",
       "          1.00687468, 1.00999682, 0.99485187, 0.45987268, 1.00623763]]),\n",
       "  'Naive Bayes']]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 重要性\n",
    "print('\\n -------- RadomFree importances ------------\\n')\n",
    "print(rf.feature_importances_)\n",
    "print('\\n -------- GradientBoosting importances ------------\\n')\n",
    "print(grd.feature_importances_)\n",
    "print('\\n -------- Logistic Coefficient  ------------\\n')\n",
    "print(lm.coef_ )\n",
    "\n",
    "\n",
    "# 其他几款模型的特征选择\n",
    "eight_names = list(predictEight.keys())\n",
    "[[predictEight[n]['importance'],n] for n in eight_names if predictEight[n]['importance'] != [] ]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 6、14套模型的ROC值计算与plot\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(1)\n",
    "plt.plot([0, 1], [0, 1], 'k--')\n",
    "plt.plot(fpr_lm, tpr_lm, label='LR')\n",
    "plt.plot(fpr_rt_lm, tpr_rt_lm, label='RT + LR')\n",
    "plt.plot(fpr_rf, tpr_rf, label='RF')\n",
    "plt.plot(fpr_rf_lm, tpr_rf_lm, label='RF + LR')\n",
    "plt.plot(fpr_grd, tpr_grd, label='GBT')\n",
    "plt.plot(fpr_grd_lm, tpr_grd_lm, label='GBT + LR')\n",
    "# 8 款模型\n",
    "for (fpr,tpr),name in [[predictEight[n]['fpr_tpr'],n] for n in eight_names] :\n",
    "    plt.plot(fpr, tpr, label=name)\n",
    "    \n",
    "\n",
    "plt.xlabel('False positive rate')\n",
    "plt.ylabel('True positive rate')\n",
    "plt.title('ROC curve')\n",
    "plt.legend(loc='best')\n",
    "plt.show()\n",
    "\n",
    "plt.figure(2)\n",
    "plt.xlim(0, 0.2)\n",
    "plt.ylim(0.4, 1)     # ylim改变     # matt\n",
    "plt.plot([0, 1], [0, 1], 'k--')\n",
    "plt.plot(fpr_lm, tpr_lm, label='LR')\n",
    "plt.plot(fpr_rt_lm, tpr_rt_lm, label='RT + LR')\n",
    "plt.plot(fpr_rf, tpr_rf, label='RF')\n",
    "plt.plot(fpr_rf_lm, tpr_rf_lm, label='RF + LR')\n",
    "plt.plot(fpr_grd, tpr_grd, label='GBT')\n",
    "plt.plot(fpr_grd_lm, tpr_grd_lm, label='GBT + LR')\n",
    "for (fpr,tpr),name in [[predictEight[n]['fpr_tpr'],n] for n in eight_names] :\n",
    "    plt.plot(fpr, tpr, label=name)\n",
    "plt.xlabel('False positive rate')\n",
    "plt.ylabel('True positive rate')\n",
    "plt.title('ROC curve (zoomed in at top left)')\n",
    "plt.legend(loc='best')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## optimize\n",
    "\n",
    "    借助快照集成，参考：(titu1994/Snapshot-Ensembles)[https://github.com/titu1994/Snapshot-Ensembles]\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 7、加权模型融合数据准备\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# from MinimiseOptimize import MinimiseOptimize,log_loss_func,calculate_weighted_accuracy\n",
    "\n",
    "# 集成数据准备\n",
    "preds_dict = {}\n",
    "for pred_tmp,name in [[predictEight[n]['prob_pos'],n] for n in names] + [(y_pred_lm,'LM'),\n",
    "                       (y_pred_rt,'RT + LM'),\n",
    "                       (y_pred_rf_lm,'RF + LM'),\n",
    "                       (y_pred_grd_lm,'GBT + LM'),\n",
    "                       (y_pred_grd,'GBT'),\n",
    "                       (y_pred_rf,'RF')]:\n",
    "    preds_dict[name] = np.array([1 - pred_tmp , pred_tmp]).T\n",
    "\n",
    "# 参数准备\n",
    "preds = list(preds_dict.values())\n",
    "models_filenames = list(preds_dict.keys())  # 模型个数\n",
    "sample_N,nb_classes = preds[0].shape  # 样本数/分类数\n",
    "testY = y_test.reshape((len(y_test),1))  # 真实Label (2000,1)\n",
    "testY_cat = np.array([1 - y_test ,y_test]).T # (2000,2)   "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 8、基准优化策略：14套模型融合——平均\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy :  87.1\n",
      "Recall :  0.8768920282542886\n"
     ]
    }
   ],
   "source": [
    "# 模型集成：无权重\n",
    "    # 无权重则代表权重为平均值\n",
    "    \n",
    "    \n",
    "prediction_weights = [1. / len(models_filenames)] * len(models_filenames)\n",
    "calculate_weighted_accuracy(prediction_weights)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 9、加权平均优化策略：14套模型融合——加权平均优化"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " ------- Iteration : 1  - acc: 89.9  - rec:0.9112008072653885 -------  \n",
      "    Best Ensemble Weights: \n",
      " [9.39305744e-02 8.17490213e-04 7.21274598e-18 2.76659200e-02\n",
      " 2.01474781e-01 1.09660230e-02 1.47044662e-22 0.00000000e+00\n",
      " 7.10928002e-02 5.07763599e-22 5.94052411e-01 3.11248461e-17\n",
      " 4.05696759e-18 8.68332543e-18]\n",
      "\n",
      " ------- Iteration : 2  - acc: 90.14999999999999  - rec:0.9112008072653885 -------  \n",
      "    Best Ensemble Weights: \n",
      " [1.83880000e-06 1.38943213e-03 1.75379768e-02 2.20796683e-01\n",
      " 7.58154093e-03 8.05158020e-07 2.01998644e-01 5.42057540e-08\n",
      " 5.03120855e-01 3.53512902e-02 1.83793083e-15 1.22206464e-02\n",
      " 2.72729909e-18 3.34792188e-07]\n",
      "\n",
      " ------- Iteration : 3  - acc: 89.8  - rec:0.9081735620585267 -------  \n",
      "    Best Ensemble Weights: \n",
      " [1.93249605e-03 2.35154266e-03 1.82989240e-03 2.74282797e-02\n",
      " 4.93008498e-01 1.01940297e-16 1.51749947e-17 3.68877536e-04\n",
      " 2.17449203e-01 1.43765021e-17 7.65483666e-02 1.54346619e-01\n",
      " 2.05674777e-17 2.47362257e-02]\n",
      "\n",
      " ------- Iteration : 4  - acc: 90.14999999999999  - rec:0.9182643794147326 -------  \n",
      "    Best Ensemble Weights: \n",
      " [1.23769630e-01 1.29695793e-20 5.96825566e-03 2.88649446e-01\n",
      " 4.78954205e-01 5.01184950e-17 1.02033430e-01 6.25033422e-04\n",
      " 8.49939765e-20 2.33164662e-16 9.98248415e-17 0.00000000e+00\n",
      " 3.69200692e-20 0.00000000e+00]\n",
      "\n",
      " ------- Iteration : 5  - acc: 89.95  - rec:0.9142280524722503 -------  \n",
      "    Best Ensemble Weights: \n",
      " [2.15634584e-01 7.41081366e-03 3.99586119e-02 1.91305281e-01\n",
      " 5.43334628e-01 2.60539345e-11 4.93863067e-15 2.35608824e-03\n",
      " 6.46905187e-12 1.20573857e-12 1.65579916e-11 3.84251578e-13\n",
      " 3.86851248e-10 6.67502170e-11]\n",
      "\n",
      " ------- Iteration : 6  - acc: 89.5  - rec:0.9061553985872856 -------  \n",
      "    Best Ensemble Weights: \n",
      " [1.62214736e-01 3.64948121e-04 1.50722026e-04 1.08931923e-02\n",
      " 5.14074887e-04 8.15876855e-19 2.31157160e-01 1.06136871e-04\n",
      " 7.99502531e-18 2.62169605e-01 8.10466279e-18 3.32429424e-01\n",
      " 2.69913076e-17 4.76607522e-17]\n",
      "\n",
      " ------- Iteration : 7  - acc: 89.60000000000001  - rec:0.9071644803229062 -------  \n",
      "    Best Ensemble Weights: \n",
      " [6.57548600e-03 1.54188084e-03 1.47676355e-03 1.41236072e-01\n",
      " 3.77293843e-01 6.99546419e-04 2.45567437e-04 6.75728379e-09\n",
      " 1.56607840e-03 6.44756466e-03 5.78807775e-05 4.61751113e-01\n",
      " 3.70189033e-11 1.10823302e-03]\n",
      "\n",
      " ------- Iteration : 8  - acc: 89.8  - rec:0.905146316851665 -------  \n",
      "    Best Ensemble Weights: \n",
      " [7.33217680e-06 1.50345875e-02 3.41393195e-17 4.78444060e-13\n",
      " 8.20065255e-02 2.00147535e-13 6.78159057e-02 1.87486538e-07\n",
      " 5.73718360e-07 1.55196825e-13 9.50415117e-02 2.06594672e-12\n",
      " 5.96461881e-01 1.43631495e-01]\n",
      "\n",
      " ------- Iteration : 9  - acc: 89.75  - rec:0.9041372351160444 -------  \n",
      "    Best Ensemble Weights: \n",
      " [2.15463155e-01 1.95812047e-02 1.85868558e-02 9.11366764e-02\n",
      " 4.71665034e-04 5.28887184e-03 1.32261785e-01 3.20373899e-03\n",
      " 7.52301396e-02 1.76736004e-01 2.11556070e-01 3.99847347e-02\n",
      " 1.04990984e-02 2.15391263e-13]\n",
      "\n",
      " ------- Iteration : 10  - acc: 89.75  - rec:0.9021190716448032 -------  \n",
      "    Best Ensemble Weights: \n",
      " [2.98266445e-16 3.23170157e-03 3.36119466e-03 2.84456522e-02\n",
      " 1.10016756e-01 4.88326154e-04 3.65369768e-02 1.66841256e-04\n",
      " 9.58335780e-02 1.36763510e-01 9.83784375e-17 1.36262384e-01\n",
      " 4.48893080e-01 1.52391517e-14]\n",
      "\n",
      " ------- Iteration : 11  - acc: 89.8  - rec:0.9112008072653885 -------  \n",
      "    Best Ensemble Weights: \n",
      " [8.52688338e-02 1.91939090e-07 9.48193094e-21 4.37745853e-01\n",
      " 3.57776054e-17 3.44797489e-21 7.91566439e-18 0.00000000e+00\n",
      " 8.72262296e-02 0.00000000e+00 1.29299058e-21 3.13791089e-01\n",
      " 7.59678034e-02 2.42035815e-17]\n",
      "\n",
      " ------- Iteration : 12  - acc: 89.8  - rec:0.9071644803229062 -------  \n",
      "    Best Ensemble Weights: \n",
      " [4.71358136e-02 2.02998767e-03 5.84814478e-03 2.54488287e-01\n",
      " 4.97093093e-20 7.08416193e-17 1.87021553e-02 5.19894683e-19\n",
      " 8.46014176e-02 6.52687157e-17 3.24096993e-01 2.63097201e-01\n",
      " 3.69295541e-17 5.74635291e-17]\n",
      "\n",
      " ------- Iteration : 13  - acc: 90.14999999999999  - rec:0.8950554994954592 -------  \n",
      "    Best Ensemble Weights: \n",
      " [1.18220400e-02 9.84490342e-03 5.54735600e-03 3.87888127e-04\n",
      " 9.28542635e-04 1.08030615e-17 3.37277379e-02 1.95427008e-06\n",
      " 3.74700231e-01 2.67067391e-01 6.56337794e-17 3.26867725e-18\n",
      " 2.02533368e-01 9.34385876e-02]\n",
      "\n",
      " ------- Iteration : 14  - acc: 89.9  - rec:0.9071644803229062 -------  \n",
      "    Best Ensemble Weights: \n",
      " [2.25711816e-02 3.84970291e-03 1.99552088e-06 9.81366740e-02\n",
      " 9.13430937e-02 5.55359765e-03 2.13084721e-02 3.55320798e-18\n",
      " 2.06693718e-01 9.44982936e-02 3.55629701e-01 4.36461287e-02\n",
      " 3.64821971e-02 2.02852443e-02]\n",
      "\n",
      " ------- Iteration : 15  - acc: 89.95  - rec:0.9091826437941474 -------  \n",
      "    Best Ensemble Weights: \n",
      " [1.10757169e-16 1.09197639e-02 3.35763149e-17 9.75611314e-02\n",
      " 8.81936453e-02 3.35656506e-17 1.60321408e-02 1.77517062e-04\n",
      " 1.58654548e-05 4.27863399e-20 1.16017919e-01 2.45696891e-01\n",
      " 3.21123978e-01 1.04261148e-01]\n",
      "\n",
      " ------- Iteration : 16  - acc: 89.75  - rec:0.9142280524722503 -------  \n",
      "    Best Ensemble Weights: \n",
      " [3.31623902e-01 1.91709198e-06 3.29198301e-02 2.13451574e-01\n",
      " 0.00000000e+00 6.80949345e-17 1.38173406e-02 5.75274977e-15\n",
      " 2.52936334e-01 4.91227667e-16 1.55249102e-01 6.70153767e-16\n",
      " 0.00000000e+00 4.94872506e-16]\n",
      "\n",
      " ------- Iteration : 17  - acc: 90.4  - rec:0.9122098890010091 -------  \n",
      "    Best Ensemble Weights: \n",
      " [5.83558955e-02 0.00000000e+00 6.83832970e-08 3.42688873e-01\n",
      " 2.54579475e-15 1.05072861e-02 1.39125275e-15 6.61282315e-04\n",
      " 2.77075552e-01 6.14875080e-16 1.56667449e-13 4.42179074e-15\n",
      " 3.10711043e-01 7.70735231e-15]\n",
      "\n",
      " ------- Iteration : 18  - acc: 90.3  - rec:0.9101917255297679 -------  \n",
      "    Best Ensemble Weights: \n",
      " [1.63645116e-03 7.38511066e-04 5.43294397e-04 3.67886893e-01\n",
      " 1.30709840e-01 3.21469937e-03 4.55991582e-02 2.37448838e-04\n",
      " 4.29583044e-01 1.11524265e-14 5.56678730e-03 3.52405083e-15\n",
      " 1.01803609e-02 4.10351195e-03]\n",
      "\n",
      " ------- Iteration : 19  - acc: 90.0  - rec:0.8920282542885973 -------  \n",
      "    Best Ensemble Weights: \n",
      " [1.50218168e-02 2.17453416e-02 3.12792427e-03 7.12889736e-02\n",
      " 1.72156379e-02 9.94134239e-17 1.80906886e-02 2.00771573e-03\n",
      " 1.80771373e-02 2.61471678e-01 3.01721894e-17 2.71304827e-17\n",
      " 2.89783132e-01 2.82169954e-01]\n",
      "\n",
      " ------- Iteration : 20  - acc: 89.75  - rec:0.9061553985872856 -------  \n",
      "    Best Ensemble Weights: \n",
      " [1.76582221e-01 2.01092295e-02 0.00000000e+00 4.86941343e-02\n",
      " 2.84629436e-01 9.12140951e-02 1.53626833e-01 9.51848222e-03\n",
      " 2.05854999e-01 9.77056998e-03 1.06253021e-16 0.00000000e+00\n",
      " 2.29610764e-21 3.34995340e-17]\n"
     ]
    }
   ],
   "source": [
    "# 模型集成：附权重\n",
    "best_acc,best_weights = MinimiseOptimize(preds,models_filenames,nb_classes,sample_N,testY,NUM_TESTS = 20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best Accuracy :  90.4\n",
      "Best Weights :  [5.83558955e-02 0.00000000e+00 6.83832970e-08 3.42688873e-01\n",
      " 2.54579475e-15 1.05072861e-02 1.39125275e-15 6.61282315e-04\n",
      " 2.77075552e-01 6.14875080e-16 1.56667449e-13 4.42179074e-15\n",
      " 3.10711043e-01 7.70735231e-15]\n",
      "Accuracy :  90.4\n",
      "Recall :  0.9122098890010091\n"
     ]
    }
   ],
   "source": [
    "# 附权重的最终结果展示\n",
    "print(\"Best Accuracy : \", best_acc)\n",
    "print(\"Best Weights : \", best_weights)\n",
    "calculate_weighted_accuracy(best_weights)"
   ]
  }
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